

1. Confidential Data (Not included in the Disk): The primary data for this paper was obtained on a confidential basis that I am unable to reproduce for replication. To obtain these data, please contact the appropriate person at the ORG-IMS Retail Pharmacy Audit (I worked with Gauri Pathak in 2003 but I am not aware of whether she still works there and if not, who her replacement is). However, details on the variables available in this data and how to obtain it are available below: 

a. ORG-IMS Retail Pharmacy Audit: All data on market shares, prices and product characteristics constructed from ORG-IMS�s retail audit data. Product-level data for 2001-2003 is available at the monthly and regional level but because of research budget limitations, more aggregate nation-wide quarterly data was purchased for this porject. Apart from product-level revenue and sales (in qty), the audit also lists characteristics such as the pack size and dosage of each product and the launch date of each product. More information on their retail audits is available on their website: http://www.orgims.co.in/ 

b. ORG-IMS Prescription Audit: This data tracks prescriptions by doctors of different specializations in a large cross-section of towns and cities in India. Annual data on the total number of nation-wide prescriptions for 2003 for 34 indications (or diseases) belonging to 14 indication groups was used in the paper. The purpose of this data was used to construct an exogenous measure of market size, which was equal to number of prescriptions times the average length of drug therapy. The 34 diseases, their indication groups, and the length of drug therapy is identified in the file prescription_length.xls.  More information on their prescription audits is available on their website: http://www.orgims.co.in/ 
 

2. Description of Non-Confidential Data (Included):  
                                      	
a. daily_dose.xls: Variables in this file are "Therapeutic Group", "Indication Group", "Pharmacological Group", "Molecule" (all as defined in the text of the paper) and "Average Daily Dose (in mg/ml)" , which is computed from Drug Facts and Comparison and in a few cases, online pharmacological sources. The purpose of the data is to convert each product, quantified in terms of mg or ml in the original data, into an average daily dose. This facilitates comparisons across different drugs which are substitutes for each other. 

b. length_prescription.xls: Variables in this file are �Indication�, �Indication Group� (both as defined in the text of the paper) and �Average Treatment Length (in days)�, which is the average number of days of drug therapy for the indication from Drug Facts and Comparisons and in a few cases, from online medical sources. The purpose of the data is to multiply by the number of prescriptions to get the number of prescribed patient-day doses (the exogenous market size).

3. Description of Code (Stata do-files): 

a.demand.txt: Computes average firm-level price, all firm characteristics and all other variables needed for demand regressions (including instruments). Includes codes for the OLS and IV regressions. 
b. suppeqn.txt: Computes marginal costs using predicted shares (formula provided in paper) and firm characteristics for drugs not under priced control. Code for regressions provided. 
c.supply_pricecont.txt: Computes marginal cost for drugs under price control. Code for regression provided. 
d. fixed_cost.txt: Computes fixed costs using the symmetric single-product firm model described in the paper. The code first generates shares, prices and product characteristics for the symmetric single-product firm model and then employs interval regressions to estimate fixed costs.
e. welfare_simul: runs welfare simulations of price deregulation and patent enforcement. Uses data on symmetric single-product firm model and fixed costs developed by code in "fixed_cost.txt"





